Intent-driven network analytics for predictive network slice management

ABSTRACT

Described are examples for providing intent based network slice management using a management data analytics function (MDAF) to predict deficiencies. A network management system receives an intent for a network slice constituent. The network management system configures computing resources for the network slice constituent to satisfy the intent based on expected performance of the computing resources. The network management system receives feedback with respect to actual performance of the network slice constituent. The network management system determines, based on analysis of the feedback by a management data analytics function (MDAF), a predicted deficiency of the network slice constituent not being able to satisfy the intent. The network management system modifies the configuration of the computing resources based on the feedback and the predicted deficiency to satisfy the intent.

BACKGROUND

A radio access network (RAN) may provide multiple user devices withwireless access to a network. The user devices may wirelesslycommunicate with a base station, which forwards the communicationstowards a core network. A core network may include multiple nodes orfunctions. For example, a 5G core network may include one or more Accessand Mobility Management Functions (AMFs), Session Management Functions(SMFs), and a User Plane Functions (UPFs). For instance, the AMF may bea control node that processes the signaling between the UEs and the corenetwork. Generally, the AMF provides quality of service (QoS) flow andsession management. All user Internet protocol (IP) packets aretransferred through the UPF. The UPF provides UE IP address allocationas well as other functions. The UPF may be connected to IP Services. TheIP Services may include the Internet, an intranet, an IP MultimediaSubsystem, a packet switched (PS) Streaming Service, and/or other IPservices.

A virtualized radio access network may utilize datacenters with genericcomputing resources for performing RAN processing for network functions.For example, instead of performing PHY and MAC layer processing locallyon dedicated hardware, a virtualized radio access network may forwardradio signals from the radio units to an edge datacenter for processingand similarly forward signals from the edge datacenter to the radiounits for wireless transmission. As another example, core networkfunctions may be implemented on generic cloud resources at variousdatacenters. Because the network datacenters utilize generic computingresources, a virtualized RAN may provide scalability and fault tolerancefor network processing. Conventionally, whether using dedicated hardwareor more generic computing resources, network configuration has beenperformed by pushing a network configuration down to lower levelmanagement functions until each network function is configured.

In complex systems, such as cellular networks in general and incloud-based virtualized deployments specifically, variations in systemresources and network conditions may result in network configurationsthat are deficient in terms of performance or efficiency. Techniques toadapt network configurations to changing conditions may be desirable.

SUMMARY

The following presents a simplified summary of one or more aspects inorder to provide a basic understanding of such aspects. This summary isnot an extensive overview of all contemplated aspects, and is intendedto neither identify key or critical elements of all aspects nordelineate the scope of any or all aspects. Its sole purpose is topresent some concepts of one or more aspects in a simplified form as aprelude to the more detailed description that is presented later.

In some aspects, the techniques described herein relate to a method ofnetwork configuration, including: receiving, at a network managementfunction, an intent for a network slice constituent; configuringcomputing resources for the network slice constituent to satisfy theintent based on expected performance of the computing resources; receivefeedback with respect to actual performance of the network sliceconstituent; determining, based on analysis of the feedback by amanagement data analytics function (MDAF), a predicted deficiency of thenetwork slice constituent not being able to satisfy the intent; andmodifying the configuration of the computing resources based on thefeedback and the predicted deficiency to satisfy the intent.

In some aspects, the techniques described herein relate to a method,wherein receiving the intent includes receiving an intent including alower threshold and an upper threshold for performance of the networkslice constituent.

In some aspects, the techniques described herein relate to a method,wherein receiving feedback with respect to actual performance of thenetwork slice constituent includes monitoring the performance of thenetwork within the lower threshold and the upper threshold.

In some aspects, the techniques described herein relate to a method,wherein determining the deficiency of the network slice constituent notbeing able to satisfy the intent includes receiving a prediction that alikelihood of satisfying the intent is less than a threshold.

In some aspects, the techniques described herein relate to a method,wherein determining the predicted deficiency of the network sliceconstituent includes training a machine learning model at the MDAF topredict a demand on the network slice constituent.

In some aspects, the techniques described herein relate to a method,wherein the demand on the network slice constituent is a network trafficlevel.

In some aspects, the techniques described herein relate to a method,wherein modifying the configuration of the computing resources based onthe feedback and the predicted deficiency to satisfy the intent includesallocating additional computing resources to the network sliceconstituent to increase a capacity of the network slice constituent tosatisfy the intent.

In some aspects, the techniques described herein relate to a method,wherein the predicted deficiency is with respect to a constraint on acost or efficiency of the intent, and wherein modifying theconfiguration of the computing resources includes reducing an allocationof computing resources to satisfy the intent at a lower cost.

In some aspects, the techniques described herein relate to a method,wherein determining the predicted deficiency of the network sliceconstituent with respect to the intent includes receiving a recommendedaction from the MDAF to satisfy the intent.

In some aspects, the techniques described herein relate to a method,wherein receiving feedback with respect to actual performance of thenetwork function includes receiving feedback at two or more levels of: anetwork function management function (NFMF), a network slice subnetmanagement function (NSSMF), or a network slice management function(NSMF).

In some aspects, the techniques described herein relate to a system fornetwork configuration, including: a network management functionconfigured to: receive an intent for a network slice constituent;configure computing resources for the network slice constituent tosatisfy the intent based on expected performance of the computingresources; determine, based on analysis of feedback with respect toactual performance of the network slice constituent by a management dataanalytics function (MDAF), a predicted deficiency of the network sliceconstituent not being able to satisfy the intent; and modify theconfiguration of the computing resources based on the feedback and thepredicted deficiency to satisfy the intent; and the MDAF configured to:receive feedback with respect to actual performance of the network sliceconstituent; and predict a deficiency of the network slice constituentnot being able to satisfy the intent based on analysis of the feedback.

In some aspects, the techniques described herein relate to a system,wherein the network function is configured to receive an intentincluding a lower threshold and an upper threshold for performance ofthe network slice constituent.

In some aspects, the techniques described herein relate to a system,wherein the feedback with respect to actual performance of the networkslice constituent includes performance metrics within the lowerthreshold and the upper threshold.

In some aspects, the techniques described herein relate to a system,wherein the MDAF is configured to predict a likelihood of the configurednetwork slice constituent satisfying the intent.

In some aspects, the techniques described herein relate to a system,wherein the MDAF is configured with a trained machine learning model topredict a demand on the network slice constituent.

In some aspects, the techniques described herein relate to a system,wherein the demand on the network slice constituent is a network trafficlevel.

In some aspects, the techniques described herein relate to a system,wherein the network management function is configured to allocateadditional computing resources to the network slice constituent toincrease a capacity of the network slice constituent to satisfy theintent.

In some aspects, the techniques described herein relate to a system,wherein the predicted deficiency is with respect to a constraint on acost or efficiency of the intent, and wherein the network managementfunction is configured to reduce an allocation of computing resources tosatisfy the intent at a lower cost.

In some aspects, the techniques described herein relate to a system,wherein the MDAF is configured to provide a recommended action tosatisfy the intent.

In some aspects, the techniques described herein relate to anon-transitory computer-readable medium storing computer executableinstructions for intent based network slice management, includinginstructions to: receive, at a network management function, an intentfor a network slice constituent configure computing resources for thenetwork slice constituent to satisfy the intent based on expectedperformance of the computing resources; receive feedback with respect toactual performance of the network slice constituent; determine, based onanalysis of the feedback by a management data analytics function (MDAF),a predicted deficiency of the network slice constituent not being ableto satisfy the intent; and modify the configuration of the computingresources based on the feedback and the predicted deficiency to satisfythe intent.

To the accomplishment of the foregoing and related ends, the one or moreaspects comprise the features hereinafter fully described andparticularly pointed out in the claims. The following description andthe annexed drawings set forth in detail certain illustrative featuresof the one or more aspects. These features are indicative, however, ofbut a few of the various ways in which the principles of various aspectsmay be employed, and this description is intended to include all suchaspects and their equivalents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example of an architecture for networkmanagement of a virtualized cellular network, in accordance with aspectsdescribed herein.

FIG. 2 is a diagram of an example of network slice based management of avirtualized cellular network, in accordance with aspects describedherein.

FIG. 3 is diagram of example intents, configurations, and feedback, inaccordance with aspects described herein.

FIG. 4 is a schematic diagram of an example of a device for intent basednetwork slice management, in accordance with aspects described herein.

FIG. 5 is a flow diagram of an example of a method of managing networkslice constituents, in accordance with aspects described herein.

FIG. 6 is a schematic diagram of an example of a device for performingfunctions described herein, in accordance with aspects described herein.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of various configurations and isnot intended to represent the only configurations in which the conceptsdescribed herein may be practiced. The detailed description includesspecific details for the purpose of providing a thorough understandingof various concepts. However, it will be apparent to those skilled inthe art that these concepts may be practiced without these specificdetails. In some instances, well-known components are shown in blockdiagram form in order to avoid obscuring such concepts.

The concept of intent driven network management allows a client tospecify a specific goal (or intent target) to be satisfied within a setof specific expectations (also referred to as contexts). Theintent-server, e.g., the intent-handler or the service provider,provides the intent-client with updates regarding the status of theintent. If the intent-server cannot achieve the goal specified as theintent, then the intent-server may reject the intent. If a satisfiedintent is degraded and no longer fully satisfied, the intent-servernotifies the client of the degradation. The client then may choose toupdate the intent and set a new goal.

In complex systems, such as cellular networks in general and incloud-based virtualized deployments specifically, the variations insystem resources and network conditions may result in deficiencies ofnetwork configurations. For example, a network configuration may nolonger satisfy an intent if underlying computing resources perform belowexpectations or if demand increases. Conversely, if demand falls, anetwork configuration may have allocated too many computing resourcesand operate inefficiently in terms of cost. One approach to correctingdeficiencies of a network configuration is for the intent-server toreport the deficiency and wait for the intent-client to provide anupdated intent. Such an approach, however, may be reactive and onlycorrect a deficiency after the intent is not satisfied. Further, theapproach involves communication between and intent-server and theintent-client, which may add overhead and involve decisions that arefurther from the computing resources.

In an aspect, this disclosure describes various examples related tonetwork management for virtualized cellular networks using networkanalytics to predict deficiencies of a network configuration forsatisfying an intent. For example, network management functions mayconfigure network functions within a 5G radio access network (RAN)and/or 5G core network. A network management function may modify thenetwork configuration to satisfy the intent without receiving an updatedintent from the intent-client. A management data analytics function(MDAF) may monitor performance of the network configuration with respectto the intent to predict a deficiency. For example, a deficiency may bepredicted based on a likelihood that the network configuration will notsatisfy the intent. In some implementations, the MDAF may include amachine-learning model to predict demand for network services. Theprediction of the deficiency may be based on changes to the predicteddemand. For example, an increase in demand may result in a currentconfiguration having insufficient resources to satisfy the intent. Thenetwork management function may modify the configuration of thecomputing resources based on the feedback and the predicted deficiencyto satisfy the intent.

In an aspect, prediction of deficiencies by a MDAF may improveperformance of network management functions and the network itself. Forexample, by predicting deficiencies, the MDAF may allow a lower levelmanagement function to modify a network configuration without waitingfor a higher level management function to respond to a degraded intent.Accordingly, the deficiency may be prevented from occurring or resolvedmore quickly in comparison to a larger reconciliation loop involvingupdated intents from higher level management functions. Further, lesscommunication may be needed between management functions, reducingoverhead of such communications. In some implementations, prediction ofa deficiency with respect to a constraint may allow the networkconfiguration to be regularly improved, for example, to operate at alower cost or with greater efficiency.

Turning now to FIGS. 1-6 , examples are depicted with reference to oneor more components and one or more methods that may perform the actionsor operations described herein, where components and/oractions/operations in dashed line may be optional. Although theoperations described below in FIG. 5 are presented in a particular orderand/or as being performed by an example component, the ordering of theactions and the components performing the actions may be varied, in someexamples, depending on the implementation. Moreover, in some examples,one or more of the actions, functions, and/or described components maybe performed by a specially-programmed processor, a processor executingspecially-programmed software or computer-readable media, or by anyother combination of a hardware component and/or a software componentcapable of performing the described actions or functions.

FIG. 1 is a diagram of an example of an architecture for management of avirtualized cellular network 100. The virtualized cellular 100 may beimplemented on a cloud network 105 to provide access for user equipment(UEs) 104. The virtualized cellular 100 may include radio units 110, oneor more edge datacenters 120, one or more datacenters 130, a networkmanagement system 140, and an MDAF 160.

The radio units 110 may include antennas configured to transmit and/orreceive radio frequency (RF) signals. In some implementations, the radiounits 110 may include RF processing circuitry. For example, the radiounits 110 may be configured to convert the received RF signals tobaseband samples and/or convert baseband samples to RF signals. Theradio units 110 may be connected to the edge datacenter 120 viafront-haul connections 116. The front-haul connections 116 may be wiredconnections such as fiber optic cables.

The edge datacenter 120 may include computing resources 122 and a switch124, which may be connected to RUs 110 via the front-haul connections116. The edge datacenter 120 may provide a virtualized base station forperforming RAN processing for one or more cells. For example, thecomputing resources 122 may be hardware servers or virtual servers. Theservers may be generic computing resources that can be configured toperform specific RAN protocol stacks including, for example, physical(PHY) layer, media access control (MAC) layer protocol stacks, radiolink control (RLC) layer, and a radio resource control (RRC) layer. Insome implementations, PHY layer processing may be more resourceintensive than higher layer processing and may benefit from performanceclose to the RUs 110. The computing resources 122 may be connected tothe switch 124 and to each other via connections, which may be wiredconnections such as Ethernet.

The datacenter 130 may include computing resources 132. Unlike the edgedatacenter 120, the datacenter 130 may lack a direct connection to RUs110. Generally, the datacenter 130 may be more centrally located, beconnected to multiple other datacenters, and/or have greater computingresources 132 than an edge datacenter 120. In some implementations,higher layer network functions and/or core network functions may beperformed at a datacenter 130. For example, the datacenter 130 mayinstantiate network functions 133 such one or more Access and MobilityManagement Functions (AMFs) 134, a Session Management Function (SMF)136, and a User Plane Function (UPF) 138.

The network management system 140 may provide a network operator withtools for configuring the virtualized cellular network 100. In anaspect, the network management system 140 provides intent basedconfiguration of the virtualized cellular network 100. An intentspecifies the expectations including requirements, goals, andconstraints for a specific service or network management workflow. Anintent is typically understandable by humans, and also can beinterpreted by a machine without any ambiguity. In contrast to animperative configuration that specifies how a network or component is toperform, an intent expresses what a network should achieve. For example,an intent may express the metrics that are be achieved and not how toachieve the metrics.

In an aspect, the network management system 140 includes one or morenetwork management functions 142. Each network management function 142may receive an intent and output one or more lower-level intentexpectations or a configuration. For example, the network managementfunction 142 may include an intent interface 144 configured to receivean intent for a network slice constituent. The network managementfunction 142 may include a configuration component 146 configured toconfigure computing resources for a network slice constituent to satisfythe intent based on expected performance of the computing resources. Thenetwork management function 142 may include an MDAF interface 148configured to determine, based on analysis of feedback with respect toactual performance of the network slice constituent by the MDAF 160, adeficiency of the network slice constituent not being able to satisfythe intent. The network management function 142 may include amodification component 150 configured to modify the configuration of thecomputing resources based on the feedback and the deficiency to satisfythe intent.

In some implementations, the network management functions 142 are slicebased network management functions arranged in a hierarchical order. Forinstance, the network management functions 142 may include acommunication service management function (CSMF), network slicemanagement function (NSMF), a network slice subnet management function(NSSMF), or a network function management function (NFMF). The slicebased network management functions may manage network constituents suchas a slice, a slice subnet, or a network function (NF). Each managementfunction 142 may provide an intent expectation for a network constituentto a lower level network management function and/or to a NF, whichreceives the intent expectation as an intent. For example, the CSMF mayprovide an intent for one or more slices to the NSMF, which may providean intent for one or more slice sub-nets to the NSSMF. The NSSMF mayprovide an intent for one or more NFs to the NFMF. The NFMF mayinstantiate the NFs on the computing resources 122, 132 at thedatacenters 120, 130 (possibly via an infrastructure service managementsystem) and communicate with the active NFs.

In some implementations, the network management system 140 may beimplemented on cloud resources such as a datacenter 130. In someimplementations, the MDAF 160 may also be implemented on the cloudresources, and there may be a logical divide between the networkmanagement system 140 and the MDAF 160. In other implementations, thenetwork management system 140 may be external to the cloud network 105and may communicate with the MDAF 160 via a network connection.

The MDAF 160 may be configured to monitor a status of the computingresources 122, 132 and/or network functions deployed on the computingresources 122, 132. The MDAF 160 may collect metrics generated by thecloud network 105 (e.g., data rates, processor/memory utilization)and/or metrics generated by network functions (e.g., number of UEs,latency, throughput). In some implementations, the MDAF 160 may beassociated with a level of network management functions. The MDAF 160may collect network status information and/or metrics relevant to theassociated level of network management functions. For instance, anetwork slice (NS) level MDAF may collect status information and/ormetrics for network slices and a network slice sub-net (NSS) level MDAFmay collect status information and/or metrics for slice sub-nets.

The MDAF 160 may include a monitoring component 162 configured toreceive feedback with respect to actual performance of the network sliceconstituent. The MDAF 160 may optionally include a prediction component164 configured to predict that a likelihood of satisfying the intent isless than a threshold. The MDAF 160 may optionally include a demandmodel 166 configured to predict a demand on the network sliceconstituent. The MDAF 160 may optionally include a recommendationcomponent 168 configured to provide a recommended action to the networkmanagement function 142 to satisfy the intent.

FIG. 2 is a diagram 200 of an example of network slice based managementand analytics of a virtualized cellular network. The network managementsystem 140 may include hierarchical management functions 142. Forexample, the management functions 142 may include a CSMF 210, a NSMF220, a NSSMF 230, and a NFMF 240. Each management function 142 may be anintent based management function that receives an intent from a higherlevel and generates an intent for a lower level and/or a configurationof NFs 250. For example, the CSMF 210 may receive an intent (e.g., for aservice) from a network operator and generate an intent 212 for the NSMF220. The NSMF may receive the intent 212 as a new intent (e.g., for anetwork slice) and generate one or more intents 222 for the NSSMF (e.g.,intent for various subnets). The NSSMF 230 may receive the intent 222 asa new intent for a subnet and generate one or more intent 232 for theNFMF 240 (e.g., intent defining required network functions). The NFMF240 may receive the intent 232 and generate configurations 242 for NFs250.

In some implementations, any of the NSMF 220, NSSMF 230, or NFMF 240 mayinclude the MDAF interface 148 for communicating with the MDAF 160 todetermine a deficiency of the network slice constituent not being ableto satisfy the intent. For example, a network management function mayprovide an intent to the MDAF 160 to monitor for deficiencies andreceive a prediction of a deficiency and/or a recommendation from theMDAF 160.

In some implementations, MDAF 160 may operate at various levelscorresponding to the hierarchical network management functions 142(e.g., MDAF-NS level 282, MDAF-NSS level 284, and MDAF-NF level 286).For example, the MDAF 160 may include a separate component for eachlevel that calculates metrics or performs analysis relevant to thelevel. The different levels may access a common pool of monitoringinformation such as measurements or data streams from the NFs 250 orunderlying computing resources. In some implementations, higher levelsof the MDAF 160 may provide analysis results to lower levels of the MDAF160. For example, the MDAF-NSS level 284 may provide a prediction ofnetwork load to the MDAF-NF level 286. Each level of the MDAF 160 mayprovide a prediction to the corresponding network management functionwith respect to a configured network slice constituent not being able tosatisfy the intent. For example, the MDAF-NSS level 284 may provide aprediction 224 with respect to the intent 222 for a network slicesubnet, or the MDAF-NF level 286 may provide a prediction 234 withrespect to the intent 232 for a network function.

FIG. 3 is a diagram 300 illustrating modification of a configuration ofnetwork resources based on a predicted deficiency. For example, anetwork management function 142 such as the NFMF 240 may receive anintent 310 for a network function such as the UPF 138. For instance, theintent 310 may include a requirement 312 for a UPF with a goal 314 of acapacity greater than X (lower threshold) and less than Y (higherthreshold). The capacity goal 314 may be, for example, a cumulativethroughput for a service or an additional throughput capacity for theservice. Further, the intent 310 may specify a constraint 316 of a costless than Z.

The NFMF 240 may be responsible for satisfying the intent 310 byconfiguring a UPF 138. The NFMF 240 may generate the UPF configuration330 for a virtualized UPF. For instance, the NFMF 240 may instantiatethe virtualized UPF on the cloud network 105 by providing the UPFconfiguration 330 to a cloud infrastructure service management (CISM)system. The UPF configuration 330 may specify levels of computingresources such as compute resources 334, storage resources 336, andtransport resources 338 for providing the virtualized UPF. The NFMF 240may determine the computing resources 332 based on expected performanceof the computing resources. For instance, the expected performance maybe based on a nominal performance indicated by the CISM, the performanceof other network slices, or historical performance data. In someimplementations, the configuration 330 may include a constraint 340 suchas a maximum cost.

In an aspect, the MDAF 160 may receive information with respect to aperformance of network resources or a status of the network. Forexample, the MDAF 160 may receive monitoring information 350 from thecomputing resources 122, 132 and/or the network management function 142.In some implementations, the monitoring information 350 may includeperformance or status information regarding compute resources, storageresources, or transport resources in the network 105. For instance, themonitoring information 350 may indicate a status such as available,degraded, or unavailable for each type of resource in one or moreregions or at specific datacenters. The performance information mayspecify one or more relevant performance metrics such as compute CPUutilization, storage capacity, or transport bandwidth. The MDAF 160 mayalso collect information regarding the status or performance of networkconstituents such as NFs 250, network slice subnets, or network slices.

The MDAF 160 may analyze feedback with respect to actual performance ofthe network slice constituent to predict a deficiency. The MDAF 160 mayprovide a prediction 360 of the deficiency. The feedback may include,for example, measured metrics corresponding to the capacity goal 314 ofthe intent. For instance, the MDAF 160 may monitor an actual throughputcapacity 352 of the UPF 138. In some implementations, the feedback mayremain within the goals or thresholds defined by the intent 310. Thatis, the MDAF 160 may not only observe a deficiency of not being able tosatisfy the intent 310, the MDAF may predict that a deficiency is likelyto occur even though the intent is currently satisfied. For example, theMDAF 160 may include one or more machine-learning models for predictingperformance of a network constituent. In some implementations, the MDAF160 may include a demand model 166. The demand model 166 may be trainedon historical usage patterns for a service. In some implementations, thedemand model 166 may operate at a higher level than the networkconstituent. For example, a demand model for a network slice or networkslice-subnet may predict demand 354 on a UPF 138 based on load on othernetwork functions. For example, an increase in activity at AMF 134 orSMF 136 (e.g., additional devices connecting or requesting sessions) maypredict greater demand (i.e., greater network traffic) on UPF 138. Asanother example, a demand model may provide time based predictions ofdemand for a service (e.g., declining usage of a navigation serviceafter rush hour).

In some implementations, the MDAF 160 may include the recommendationcomponent 168 to provide a recommendation 362 based on a predicteddeficiency. For example, in response to a predicted drop in demand, inaddition to predicting that a current configuration is likely to notsatisfy an intent, the recommendation component 168 may provide arecommendation 362 such as to decrease allocated transport resources toreduce a cost.

The modification component 150 may modify the configuration 330 inresponse to the prediction 360 and/or the recommendation 362. Forexample, in response to the predicted drop in demand and therecommendation to decrease allocated transport resources, themodification component 150 may generate the UPF configuration 370 havingallocated compute resources 334 and transport resources 338 lower thanthe UPF configuration 330. Accordingly, the UPF 138 may be more likelyto satisfy the constraint 316 on the cost.

FIG. 4 is a schematic diagram of an example of a device 400 (e.g., acomputing device) for network configuration. The device 400 may be anexample of a computing resource 132 such as a server at a datacenter 130that hosts the network management system 140 and/or the MDAF 160. Thedevice 400 is connected to other servers within the datacenter via aswitch 422 and may be connected to servers at other datacenters.

In an example, device 400 can include one or more processors 402 and/ormemory 404 configured to execute or store instructions or otherparameters related to providing an operating system 406, which canexecute one or more applications or processes, such as, but not limitedto, at least one of a network management function 142 or an MDAF 160.For example, processor 402 and memory 404 may be separate componentscommunicatively coupled by a bus (e.g., on a motherboard or otherportion of a computing device, on an integrated circuit, such as asystem on a chip (SoC), etc.), components integrated within one another(e.g., processor 402 can include the memory 404 as an on-boardcomponent), and/or the like. Memory 404 may store instructions,parameters, data structures, etc. for use/execution by processor 402 toperform functions described herein.

In an example, the network management system 140 may optionally includeone or more network management functions 142 (e.g., CSMF 210, NSMF 220,NSSMF 230, or NFMF 240), each network management function 142 includingan intent interface 144, a configuration component 146, an MDAFinterface 148, and a modification component 150.

In an example, the MDAF 160 may include the monitoring component 162.The MDAF 160 may optionally include one or more of the predictioncomponent 164, the demand model 166, or the recommendation component168.

FIG. 5 is a flow diagram of an example of a method 500 for networkconfiguration based on network analytics. For example, the method 500can be performed by a device 400 and/or one or more components thereofto configure and maintain one or more network slice constituents (e.g.,network functions 133, 250) within a cloud network 105 to provide anetwork service. For instance, the method 500 may be performed by adevice implementing a network management function 142 and/or the MDAF160.

At block 510, the method 500 may include receiving, at a networkmanagement function, an intent for a network slice constituent. In anexample, the network management function 142 and/or the intent interface144, e.g., in conjunction with processor 402, memory 404, and operatingsystem 406, can receive an intent 310 for a network slice constituent.In some implementations, at sub-block 512, the block 510 may includereceiving an intent including a lower threshold (X) and an upperthreshold (Y) for a performance metric of the network slice constituent.

At block 520, the method 500 may include configuring computing resourcesfor the network slice constituent to satisfy the intent based onexpected performance of the computing resources. In an example, thenetwork management function 142 and/or the configuration component 146,e.g., in conjunction with processor 402, memory 404, and operatingsystem 406, can configure computing resources 122, 132 for the networkslice constituent (e.g., UPF 138) to satisfy the intent 310 based onexpected performance of the computing resources.

At block 530, the method 500 includes receiving feedback with respect toactual performance of the network slice constituent. In an example, thenetwork management function 142, the MDAF 160 and/or the monitoringcomponent 162, e.g., in conjunction with processor 402, memory 404, andoperating system 406, can receive feedback with respect to actualperformance of the network slice constituent. For instance, in someimplementations, a network management function (e.g., NFMF 240 maycommunicate with the network slice constituent (e.g., NF 250) to receivefeedback such as performance metrics. In some implementations, the MDAF160 and/or monitoring component 162 may additionally or alternativelyreceive feedback. For example, in some implementations, the MDAF 160 mayreceive performance information from the computing resources 122, 132instantiating the network slice constituents. In some implementations,at sub-block 532, the block 530 may optionally include monitoring theperformance of the network within the lower threshold and the upperthreshold. That is, the feedback may indicate that the network sliceconstituent is currently satisfying the intent. In some implementations,at sub-block 534, the block 530 may optionally include receivingfeedback at two or more levels of a NFMF 240, a NSSMF 230, or a NSMF220. For instance, the MDAF-NF level 286, the MDAF-NSS level 284, and/orthe MDAF-NS level 282 may receive feedback.

At block 540, the method 500 includes determining, based on analysis offeedback with respect to actual performance of the network sliceconstituent by a MDAF, a predicted deficiency of the network sliceconstituent not being able to satisfy the intent. In an example, thenetwork management function 142, the MDAF interface 148, the MDAF 160and/or the prediction component 164, e.g., in conjunction with processor402, memory 404, and operating system 406, can determine, based onanalysis of feedback with respect to actual performance of the networkslice constituent, a predicted deficiency of the network sliceconstituent not being able to satisfy the intent. In someimplementations, at sub-block 542 the block 540 may optionally includereceiving a prediction 360 that a likelihood of satisfying the intent isless than a threshold. For instance, the network management function 142may receive the prediction 360 from the MDAF 160 and/or the predictioncomponent 164 via the MDAF interface 148. In some implementations, atsub-block 544, the block 540 may optionally include training a machinelearning model at the MDAF to predict a demand on the network sliceconstituent. For instance, the MDAF 160 may train the demand model 166to predict the demand on the network slice constituent. For example, thedemand on the network slice constituent may be a network traffic level.In some implementations, at sub-block 546, the block 540 may optionallyinclude receiving a recommended action from the MDAF 160 to satisfy theintent. For example, the network management function 142 may receive therecommendation 362 from the MDAF 160 and/or the recommendation component168 via the MDAF interface 148.

At block 550, the method 500 includes modifying the configuration of thecomputing resources based on the feedback and predicted the predicteddeficiency to satisfy the intent. In an example, the network managementfunction 142 and/or the intent modification component 150, e.g., inconjunction with processor 402, memory 404, and operating system 406,can modify the configuration 330 of the computing resources 122, 132based on the feedback and the predicted deficiency to satisfy the intent310. In some implementations, at sub-block 552, the block 550 mayoptionally include allocating additional computing resources to thenetwork slice constituent to increase a capacity of the network sliceconstituent to satisfy the intent 310. In some implementations, atsub-block 554, the block 550 may optionally include reducing anallocation of computing resources to satisfy the intent 310 at a lowercost.

FIG. 6 illustrates an example of a device 600 including additionaloptional component details as those shown in FIG. 4 . In one aspect,device 600 may include processor 602, which may be similar to processor402 for carrying out processing functions associated with one or more ofcomponents and functions described herein. Processor 602 can include asingle or multiple set of processors or multi-core processors. Moreover,processor 602 can be implemented as an integrated processing systemand/or a distributed processing system.

Device 600 may further include memory 604, which may be similar tomemory 404 such as for storing local versions of operating systems (orcomponents thereof) and/or applications being executed by processor 602,such as the network management system 140, the MDAF 160, etc. Memory 604can include a type of memory usable by a computer, such as random accessmemory (RAM), read only memory (ROM), tapes, magnetic discs, opticaldiscs, volatile memory, non-volatile memory, and any combinationthereof.

Further, device 600 may include a communications component 606 thatprovides for establishing and maintaining communications with one ormore other devices, parties, entities, etc. utilizing hardware,software, and services as described herein. Communications component 606may carry communications between components on device 600, as well asbetween device 600 and external devices, such as devices located acrossa communications network and/or devices serially or locally connected todevice 600. For example, communications component 606 may include one ormore buses, and may further include transmit chain components andreceive chain components associated with a wireless or wired transmitterand receiver, respectively, operable for interfacing with externaldevices.

Additionally, device 600 may include a data store 608, which can be anysuitable combination of hardware and/or software, that provides for massstorage of information, databases, and programs employed in connectionwith aspects described herein. For example, data store 608 may be or mayinclude a data repository for operating systems (or components thereof),applications, related parameters, etc.) not currently being executed byprocessor 602. In addition, data store 608 may be a data repository fornetwork management system 140, MDAF 160, etc.

Device 600 may optionally include a user interface component 610operable to receive inputs from a user of device 600 and furtheroperable to generate outputs for presentation to the user. Userinterface component 610 may include one or more input devices, includingbut not limited to a keyboard, a number pad, a mouse, a touch-sensitivedisplay, a navigation key, a function key, a microphone, a voicerecognition component, a gesture recognition component, a depth sensor,a gaze tracking sensor, a switch/button, any other mechanism capable ofreceiving an input from a user, or any combination thereof. Further,user interface component 610 may include one or more output devices,including but not limited to a display, a speaker, a haptic feedbackmechanism, a printer, any other mechanism capable of presenting anoutput to a user, or any combination thereof.

Device 600 may additionally include a network management system 140 forconfiguring network resources for a network slice constituent to satisfyan intent based on expected performance of the computing resources, anMDAF 160 for predicting a deficiency of the network slice constituentnot being able to satisfy the intent, etc., as described herein.

By way of example, an element, or any portion of an element, or anycombination of elements may be implemented with a “processing system”that includes one or more processors. Examples of processors includemicroprocessors, microcontrollers, digital signal processors (DSPs),field programmable gate arrays (FPGAs), programmable logic devices(PLDs), state machines, gated logic, discrete hardware circuits, andother suitable hardware configured to perform the various functionalitydescribed throughout this disclosure. One or more processors in theprocessing system may execute software. Software shall be construedbroadly to mean instructions, instruction sets, code, code segments,program code, programs, subprograms, software modules, applications,software applications, software packages, routines, subroutines,objects, executables, threads of execution, procedures, functions, etc.,whether referred to as software, firmware, middleware, microcode,hardware description language, or otherwise.

Accordingly, in one or more aspects, one or more of the functionsdescribed may be implemented in hardware, software, firmware, or anycombination thereof. If implemented in software, the functions may bestored on or encoded as one or more instructions or code on acomputer-readable medium. Computer-readable media includes computerstorage media. Non-transitory computer-readable media excludestransitory signals. Storage media may be any available media that can beaccessed by a computer. By way of example, and not limitation, suchcomputer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium that can be used to carry or store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Disk and disc, as used herein, includescompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), and floppy disk where disks usually reproduce data magnetically,while discs reproduce data optically with lasers. Combinations of theabove should also be included within the scope of computer-readablemedia.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but are to be accorded the full scope consistentwith the claim language. Reference to an element in the singular is notintended to mean “one and only one” unless specifically so stated, butrather “one or more.” Unless specifically stated otherwise, the term“some” refers to one or more. All structural and functional equivalentsto the elements of the various aspects described herein that are knownor later come to be known to those of ordinary skill in the art areintended to be encompassed by the claims. Moreover, nothing disclosedherein is intended to be dedicated to the public regardless of whethersuch disclosure is explicitly recited in the claims. No claim element isto be construed as a means plus function unless the element is expresslyrecited using the phrase “means for.”

What is claimed is:
 1. A method of network configuration, comprising:receiving, at a network management function, an intent for a networkslice constituent; configuring computing resources for the network sliceconstituent to satisfy the intent based on expected performance of thecomputing resources; receive feedback with respect to actual performanceof the network slice constituent; determining, based on analysis of thefeedback by a management data analytics function (MDAF), a predicteddeficiency of the network slice constituent not being able to satisfythe intent; and modifying the configuration of the computing resourcesbased on the feedback and the predicted deficiency to satisfy theintent.
 2. The method of claim 1, wherein receiving the intent comprisesreceiving an intent including a lower threshold and an upper thresholdfor performance of the network slice constituent.
 3. The method of claim2, wherein receiving feedback with respect to actual performance of thenetwork slice constituent comprises monitoring the performance of thenetwork within the lower threshold and the upper threshold.
 4. Themethod of claim 1, wherein determining the deficiency of the networkslice constituent not being able to satisfy the intent comprisesreceiving a prediction that a likelihood of satisfying the intent isless than a threshold.
 5. The method of claim 1, wherein determining thepredicted deficiency of the network slice constituent comprises traininga machine learning model at the MDAF to predict a demand on the networkslice constituent.
 6. The method of claim 5, wherein the demand on thenetwork slice constituent is a network traffic level.
 7. The method ofclaim 1, wherein modifying the configuration of the computing resourcesbased on the feedback and the predicted deficiency to satisfy the intentcomprises allocating additional computing resources to the network sliceconstituent to increase a capacity of the network slice constituent tosatisfy the intent.
 8. The method of claim 1, wherein the predicteddeficiency is with respect to a constraint on a cost or efficiency ofthe intent, and wherein modifying the configuration of the computingresources comprises reducing an allocation of computing resources tosatisfy the intent at a lower cost.
 9. The method of claim 1, whereindetermining the predicted deficiency of the network slice constituentwith respect to the intent comprises receiving a recommended action fromthe MDAF to satisfy the intent.
 10. The method of claim 1, whereinreceiving feedback with respect to actual performance of the networkfunction comprises receiving feedback at two or more levels of: anetwork function management function (NFMF), a network slice subnetmanagement function (NSSMF), or a network slice management function(NSMF).
 11. A system for network configuration, comprising: a networkmanagement function configured to: receive an intent for a network sliceconstituent; configure computing resources for the network sliceconstituent to satisfy the intent based on expected performance of thecomputing resources; determine, based on analysis of feedback withrespect to actual performance of the network slice constituent by amanagement data analytics function (MDAF), a predicted deficiency of thenetwork slice constituent not being able to satisfy the intent; andmodify the configuration of the computing resources based on thefeedback and the predicted deficiency to satisfy the intent; and theMDAF configured to: receive feedback with respect to actual performanceof the network slice constituent; and predict a deficiency of thenetwork slice constituent not being able to satisfy the intent based onanalysis of the feedback.
 12. The system of claim 11, wherein thenetwork function is configured to receive an intent including a lowerthreshold and an upper threshold for performance of the network sliceconstituent.
 13. The system of claim 12, wherein the feedback withrespect to actual performance of the network slice constituent comprisesperformance metrics within the lower threshold and the upper threshold.14. The system of claim 11, wherein the MDAF is configured to predict alikelihood of the configured network slice constituent satisfying theintent.
 15. The system of claim 11, wherein the MDAF is configured witha trained machine learning model to predict a demand on the networkslice constituent.
 16. The system of claim 15, wherein the demand on thenetwork slice constituent is a network traffic level.
 17. The system ofclaim 11, wherein the network management function is configured toallocate additional computing resources to the network slice constituentto increase a capacity of the network slice constituent to satisfy theintent.
 18. The system of claim 11, wherein the predicted deficiency iswith respect to a constraint on a cost or efficiency of the intent, andwherein the network management function is configured to reduce anallocation of computing resources to satisfy the intent at a lower cost.19. The system of claim 11, wherein the MDAF is configured to provide arecommended action to satisfy the intent.
 20. A non-transitorycomputer-readable medium storing computer executable instructions forintent based network slice management, comprising instructions to:receive, at a network management function, an intent for a network sliceconstituent; configure computing resources for the network sliceconstituent to satisfy the intent based on expected performance of thecomputing resources; receive feedback with respect to actual performanceof the network slice constituent; determine, based on analysis of thefeedback by a management data analytics function (MDAF), a predicteddeficiency of the network slice constituent not being able to satisfythe intent; and modify the configuration of the computing resourcesbased on the feedback and the predicted deficiency to satisfy theintent.